V2V Communication Research Articles

Performance analysis of cooperative multi-relay spatial modulation based OTFS system in V2V communication

Owing to the characteristics of spatial modulation (SM) and orthogonal time frequency space (OTFS) modulation, SM based OTFS (SM-OTFS) system has an excellent capability to improve the effectiveness and reliability of vehicle-to-vehicle (V2V) communication. To further combat the severe shadow effect, improve the system performance, and extend the transmission range of V2V communication in the urban scenario with dense buildings, we propose a cooperative multi-relay SM-OTFS system with the optimal relay selection in this paper, where both amplify-and-forward (AF) and decode-and-forward (DF) protocols have been employed and only the optimal relay participates in the signal forwarding. Moreover, the fast time-selective fading is mainly considered, the closed-form outage probability and the system capacity of the cooperative SM-OTFS (CSM-OTFS) system are derived from the viewpoint of signal transmissions in the time domain. Numerical results verify the superiority of the proposed CSM-OTFS system over the non-cooperative SM-OTFS system and illustrate the influence of the main system parameters on the system outage probability and capacity, such as the relaying protocol, the number of relays, the estimation rate of channel state information (CSI), and the time-varying channel characteristic.

Vehicle-to-Vehicle Based Autonomous Flight Coordination Control System for Safer Operation of Unmanned Aerial Vehicles

The exponential growth of unmanned aerial vehicles (UAVs) or drones in recent years has raised concerns about their safe operation, especially in beyond-line-of-sight (BLOS) scenarios. Existing unmanned aircraft system traffic management (UTM) heavily relies on commercial communication networks, which may become ineffective if network infrastructures are damaged or disabled. For this challenge, we propose a novel approach that leverages vehicle-to-vehicle (V2V) communications to enhance UAV safety and efficiency in UAV operations. In this study, we present a UAV information collection and sharing system named Drone Mapper®, enabled by V2V communications, so that UAVs can share their locations with each another as well as with the ground operation station. Additionally, we introduce an autonomous flight coordination control system (AFCCS) that augments UAV safety operations by providing two essential functionalities: UAV collision avoidance and UAV formation flight, both of which work based on V2V communications. To evaluate the performance of the developed AFCCS, we conducted comprehensive field experiments focusing on UAV collision avoidance and formation flight. The experimental results demonstrate the effectiveness of the proposed system and show seamless operations among multiple UAVs.

An Improved GOMP Sparse Channel Estimation for Vehicle-to-Vehicle Communications

Reliable channel estimation is critical for wireless communication performance, especially in vehicle-to-vehicle (V2V) communication scenarios. Aiming at the major challenges of channel tracking and estimating as the highly dynamic nature of vehicle environments, an improved generalized orthogonal matching pursuit (iGOMP) is proposed for V2V channel estimation. The iGOMP algorithm transforms the channel estimation problem into a sparse signal recovery problem and replaces the classical inner product criterion with the Dice atom matching criterion. Additionally, the atomic weak progressive selection method is integrated to avoid the suboptimal selection of atoms from the redundant dictionary using the inner product criterion. The proposed iGOMP method can achieve optimal channel estimation by iterating feedback results. Simulation results demonstrate that the iGOMP method has superior estimation accuracy, mean square error (MSE), and bit error rate (BER) performance compared with traditional channel estimation methods in V2V communications.

Performance analysis of full duplex and frequency reuse in NR‐V2X networks

AbstractThis paper employs full duplex (FD) and frequency reuse (FR) to improve the spectral efficiency while considering inter‐carrier interference (ICI) in 5G new radio vehicle‐to‐everything (NR‐V2X) networks. The quality of service of vehicle‐to‐vehicle (V2V) communications in such a system is affected by the self‐interference (SI) caused by FD, the co‐channel interference (CCI) caused by FR, and ICI caused by the Doppler effect. We present a performance analysis by proposing feasibility conditions for implementing FD and FR in NR‐V2X networks. We obtain a closed‐form expression for the maximum link distance between the transmitter and receiver of a link to provide reliable V2V communications. Our simulation results reveal the interplay between FD and FR technologies and demonstrate the performance gains of our proposed scheme compared to existing schemes in the literature.

Algorithm development for Vehicle-To-Vehicle (V2V) communication

This paper presents the development of an algorithm for Vehicle-to-Vehicle (V2V) communication, a crucial technology in Intelligent Transportation Systems (ITS) that holds significant potential for enhancing road safety and traffic efficiency. One of the most common types of vehicle collisions occurs at intersections, particularly those without traffic lights. This study focuses on creating a V2V algorithm designed to prevent collisions in such scenarios. The findings were presented through visual simulations that depict various scenarios involving vehicles approaching an intersection. The algorithm follows a two-step process: Firstly, it utilizes Dedicated Short-Range Communication Systems (DSRCS) to accurately estimate the distance between vehicles. Leveraging this distance information, the algorithm dynamically adjusts the speed of each vehicle. The algorithm’s performance is assessed using Convolutional Neural Networks (CNN), which enables a comprehensive evaluation of its reliability and efficiency in V2V communication. The algorithm demonstrates notable enhancements in the reliability and efficiency of V2V communication. This paper serves as a validation of the feasibility of developing more advanced V2V communication algorithm and potentially making significant contributions to the advancement of ITS.

Stochastic Velocity Prediction for Connected Vehicles Considering V2V Communication Interruption

Stochastic Velocity Prediction for Connected Vehicles Considering V2V Communication Interruption

Cooperative Perception With Learning-Based V2V Communications

Cooperative Perception With Learning-Based V2V Communications

Event-Triggered Control with Intermittent Communications over Erasure Channels for Leader–Follower Problems with the Combined-Slip Effect

In this article, we investigate the vehicle path-following problem for a vehicle-to-vehicle (V2V)–enabled leader–follower scenario and propose an integrated control policy for the following vehicle to accurately follow the leader’s path. We propose a control strategy for the follower vehicle to maintain a velocity-dependent distance relative to the leader vehicle while stabilizing its longitudinal and lateral dynamics considering the combined-slip effect and tire force saturation. In light of reducing wireless communication errors and efficient usage of battery power and resources, we propose an intermittent V2V communication in which transmissions are scheduled based on an event-triggered law. An event is triggered and a transmission is scheduled in subsequent sample time if some of the well-defined path-following error functions (relative distance error and lateral error) exceed given tolerance bounds. Considering that the V2V communication channel might be erroneous or a transmission fails due to, e.g., vehicles’ distance or low battery power, we consider data loss in the V2V channel. Our proposed control law consists of two components: a receding horizon feedback controller with state constraints based on a safe operation envelop and a feedforward controller that generates complementary control inputs when the leader’s states are successfully communicated to the follower. To mitigate the effects of data loss on the follower’s path-following performance, we design a remote estimator for the follower to predict the leader’s state using its on-board sensor equipment when an event is triggered but the corresponding state information is not received by the follower due to a packet loss. Incorporating this estimator allows the follower to apply cautionary control inputs knowing that the path-following error had exceeded a tolerance bound. We show that while the feedback controller stabilizes the follower’s dynamics, the feedforward component improves the safety margins and reduces the path-following errors even in the presence of data loss. High-fidelity simulations are performed using CarSim to validate the effectiveness of our proposed control architecture specifically in harsh maneuvers and high-slip scenarios on various road surface conditions.

Conditional privacy-preserving authentication scheme for V2V communication without pseudonyms

Pseudonym is widely used in conditional privacy-preserving authentication (CPPA) scheme for vehicle-to-vehicle (V2V) communication in vehicular ad hoc networks (VANETs) in order to protect user’s privacy. The main disadvantage of pseudonym-based CPPA schemes is that the pseudonym has to be frequently updated, which puts a heavy burden on VANETs, due to linkage-attack. To solve this problem, we proposed a CPPA scheme for V2V communication without involving pseudonyms. The vehicle does not use any pseudonym in the proposed scheme, therefore it is immune to pseudonym updating and management problems. Moreover, the scheme enables secure V2V communication, which means that only legal vehicles have access to the transmitted messages. Security analysis shows that the scheme supports secure properties of anonymity, unlinkability, non-repudiation, message integrity, traceability, and revocation. Performance analysis and extensive simulations show that the scheme is computationally efficient and has the lowest average message delay among compared schemes.

A Novel Strong Tracking ETUKF for State Estimation of Preceding Vehicles Using V2V Communications

Real-time decision-making of the host vehicle relies on the motion information of the preceding vehicle (PV). Using some advanced sensors to estimate the PV state is a hot research topic. However, existing studies rarely use vehicle-to-vehicle (V2V) communication to estimate the PV state. Meanwhile, the influence of model parameter perturbation on estimation accuracy is not fully considered. In this article, a strong tracking event-triggered unscented Kalman filter (STETUKF) is designed to estimate the PV state utilizing V2V communication. First, we develop a nonlinear vehicle model. Then, the strong tracking filtering is combined with the ETUKF to establish STETUKF. Among them, the event-triggered mechanism determines if onboard sensor data from the PV will be delivered to the host vehicle. By appropriately adjusting the event-triggered threshold, an ideal compromise can be achieved between the transmission rate and estimation accuracy. Finally, experimental results demonstrate that STETUKF has better estimation performance than the traditional UKF. The proposed algorithm can save at least 64.93% of communication resources and it is robust to the model parameter perturbation.

A Multi-Objective Optimization-Based Transmission Design for V2V Communication Networks

Data delivery in vehicular networks confronts unique challenges due to vehicle movement and stringent quality-of-service (QoS) demands. This paper investigates the efficient transmission design problem in a typical vehicle-to-vehicle (V2V) communication network, where multiple source-destination pairs with different types of message transmission requirements coexist and the network environment dynamically changes. A sequential transmission decision framework based on the multi-objective optimization (MOO) theory is proposed to maximize the performance of each link, while ensuring the QoS of different messages. We integrate the Lyapunov optimization theory with the weighted Tchebycheff method, as well as other mathematical optimization tools, to transform the original problem into a solvable MOO problem at each time slot. Transmission actions can be determined continuously according to the environment change and time-related design goals. The effectiveness and efficiency of the proposed method are demonstrated through extensive simulation experiments.

Traffic Safety Assessment with Integrated Communication System of Connected and Automated Vehicles at Signalized Intersections

Connected and automated vehicles (CAVs) are expected to improve traffic safety effectively at signalized intersections. Considerable studies have been conducted to investigate the benefits of CAVs in improving traffic mobility and efficiency. However, in most previous research, vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communication have been considered separately rather than concurrently, to study the characteristics of CAVs, resulting in the potential of CAVs not being fully exploited and inconsistency with reality. In this paper, an integrated communication system of CAVs (ICSC), which incorporates V2V and V2I communication, is proposed, to assess traffic safety at signalized intersections. In this study, the intelligent driver model (IDM) is used to approximate V2V communication between a subject CAV and preceding CAVs. A reinforcement learning algorithm is adopted to model V2I communication between a CAV and a traffic light. The traffic safety effect of ICSC, V2V-only, and V2I-only scenarios is evaluated for different market penetration rates (MPRs). The results show that the ICSC scenario significantly reduces traffic conflicts and outperforms V2V-only, V2I-only, and benchmark scenarios when the MPR is equal to or higher than 50% with different surrogate safety measures (SSMs), such as time-exposed deceleration (TED) to avoid crashing, time exposed time-to-collision (TET), and use of a spacing gap (SGAP). Moreover, the mobility effect of the ICSC scenario is studied, and appears to increase average speed and reduce delay time. Finally, the results suggest that the ICSC can improve traffic safety and mobility concurrently and exploit the potentials of CAVs at signalized intersections.

ALAMOUTI SPACE-TIME CODING FOR VEHICULAR COMMUNICATIONS IN THE PRESENCE OF CHANNEL ESTIMATION ERRORS

In this paper, the error performance of the Alamouti space-time coding (STC) scheme is investigated for vehicle-to-vehicle (V2V) communication systems over imperfect cascaded fading channels. In vehicular communication systems, perfect knowledge of the channel state information is not available to the users at all times due to the rapid movement of communicating vehicles and fast change of the rich scattering environment which makes the fading effects in wireless channels more severe. Therefore, in the analysis, we consider the erroneous estimation of the channel gain, which is more realistic for practical scenarios. For this purpose, we first derive the moment-generation function (MGF) of the channel fading coefficient with estimation error in the case of the cascaded Nakagami-m fading conditions. Then, using the MGF, we obtain the closed-form symbol-error-rate (SER) expressions of Alamouti STC with two transmitting and L receiving antennas for the M-PSK and M-QAM modulation schemes. Then, the exact ergodic capacity expression is derived for the proposed system. Furthermore, the analytical results are verified through Monte-Carlo simulations. Numerical results show that the SER performance of V2V communication systems can be improved significantly by using Alamouti STC even in case of harsh fading conditions and full channel-state information is not available due to estimation errors.

TCaS‐VN: A novel V2V communication scheme to improve traffic control and safety in vehicular cloud networks

SummaryRoad accidents and traffic congestion are the unavoidable consequences of the growing number of vehicles on the road. Conventional management systems are not effective in mitigating these issues. However, a range of technologies, including sensors, processors, vehicle‐based resources, and mobile cloud computing, can help solve traffic and safety problems. Vehicular cloud networks, a cutting‐edge technology for the development of intelligent transportation systems, face significant challenges, such as ensuring reliable service providers that maintain stable connections between vehicles. Roadside units (RSUs) play a crucial role in these networks, but they come with several drawbacks, including high costs, imbalanced load distribution, and unreliable connections. To address these issues, we propose the Traffic Control and Safety in Vehicular Networks (TCaS‐VN) algorithm, a cloud‐based sharing services framework for vehicles that eliminates the need for RSUs. This approach offers several advantages, including a twofold improvement in the lifetime of clusters, the elimination of noncluster head nodes, a 54% reduction in overhead, a 58% reduction in service search time, and a success rate of over 95% in service provisioning.

Channel modeling for V2V communication: Enhancing road safety and intelligent transportatio

The advancement of the Internet of Things (IoT) and Vehicle-to-Vehicle (V2V) communication networks has led to increased research efforts toward developing efficient and innovative V2V communication systems. These systems are designed to achieve the goal of improving road safety and enabling intelligent transportation. A critical aspect of this effort is channel modeling. In this context, this paper summarizes the characteristics of V2V communication channels. It categorizes them into three main types: Deterministic models, Non-geometry stochastic models (NGSMs), and Geometry-based stochastic models (GBSMs). It then reviews recent literature on these three types of channel modeling methods and discusses future development directions and challenges for V2V channel modeling. The study highlights the importance of accurate channel modeling for successful V2V communication systems and emphasizes the need to consider the unique features of V2V channels. It also identifies the limitations of existing channel models and suggests areas of research that can lead to more reliable and efficient V2V communication systems.

Cooperative Perception With V2V Communication for Autonomous Vehicles

Occlusion is a critical problem in the Autonomous Driving System. Solving this problem requires robust collaboration among autonomous vehicles traveling on the same roads. However, transferring the entirety of raw sensors' data among autonomous vehicles is expensive and can cause a delay in communication. This paper proposes a method called Realtime Collaborative Vehicular Communication based on Bird's-Eye-View (BEV) map. The BEV map holds the accurate depth information from the point cloud image while its 2D representation enables the method to use a novel and well-trained image-based backbone network. Most importantly, we encode the object detection results into the BEV representation to reduce the volume of data transmission and make real-time collaboration between autonomous vehicles possible. The output of this process, the BEV map, can also be used as direct input to most route planning modules. Numerical results show that this novel method can increase the accuracy of object detection by cross-verifying the results from multiple points of view. Thus, in the process, this new method also reduces the object detection challenges that stem from occlusion and partial occlusion. Additionally, different from many existing methods, this new method significantly reduces the data needed for transfer between vehicles, achieving a speed of 21.92 Hz for both the object detection process and the data transmission process, which is sufficiently fast for a real-time system.

Secure Transmission Scheme Based on Joint Radar and Communication in Mobile Vehicular Networks

Vehicle-to-vehicle (V2V) communication applications face significant challenges to security and privacy since all types of possible breaches are common in connected and autonomous vehicles (CAVs) networks. As an inheritance from conventional wireless services, potential eavesdropping is one of the main threats to V2V communications. In our work, the anti-eavesdropping scheme in CAVs networks is developed through the use of cognitive risk control (CRC)-based vehicular joint radar-communication (JRC) system. In particular, the supplement of off-board measurements acquired using V2V links to the perceptual information has presented the potential to enhance the traffic target positioning precision. Then, transmission power control is performed utilizing reinforcement learning, the result of which is determined by a task switcher. Based on the threat evaluation, a multiple armed bandit problem is designed to implement the secret key switching procedure when it is needed. Through constant perception-execution loops (PELs), the security and confidentiality is improved for the authorized vehicles in their behavioral interactions with the illegal eavesdropper. Numerical experiments have presented that the developed approach has anticipated performance in terms of some risk assessment indicators.

Jacobian linear regression and Tate Bryant Euler angle enabled autonomous vehicle LiFi communication sustained IOT

Artificial Intelligence (AI) and the constant paradigm shift in road traffic have led to a need for significant improvement in road safety to minimize traffic accidents. LiFi helps minimize accidents by transmitting data between multiple vehicles (i.e. Vehicle-to-Vehicle (V2V)) and between vehicles and infrastructure (i.e. Vehicle-to-Infrastructure (V2I)) without interference. LiFi uses light to transmit data between devices or vehicles, which ensures efficient data transmission speed and is therefore considered a safe technology. A method called Deep Jacobian Regression and Tate Bryant Euler Recommendation (DJR-TBER) is proposed in this paper based on V2V and V2I autonomous vehicle communication. The proposed method DJR-TBER consists of an input layer, four hidden layers and finally an output layer. Sensors are first used to obtain the information. A linear regression-based speed evaluation model is developed and followed by a Jacobi matrix-based distance evaluation model in the hidden layer. The third hidden layer by developing a distance evaluation model. The use of Laplacian function ensures secure V2I communication for the autonomous vehicle. Finally, a Tate-Bryant-Euler angle-based model for emergency handling is proposed in the hidden layer to optimally consider the aspect of braking in emergency situations and thus increase driving safety.

Research on the Optimal Deployment of Expressway Roadside Units under the Fusion Perception of Intelligent Connected Vehicles

At present, there is still a lack of relevant theoretical guidance on the deployment of roadside RSU on expressways. In the face of the coexistence of V2V and V2I communication in the future, the deployment adjustment after the penetration of intelligent vehicles is not considered. Therefore, this paper proposes a roadside RSU deployment income model in consideration of the influence of V2V and V2I communication. Based on the optimal income of roadside RSU nodes, it achieves the optimization of the RSU deployment range and determines the optimal deployment spacing through the forwarding and relaying role of V2V communication so as to achieve cost savings. In terms of RSU coverage of positive income, it considers the impact of intelligent vehicles and reconstructs the traditional information flow–traffic flow coupling theory to innovatively realize the modeling of income within the information life cycle. In terms of the information transmission deficit, the WSN node energy loss model is reconstructed with permeability. Also, in terms of the construction and maintenance costs, the cost models are constructed for different cluster lengths. In order to provide a basis for expressway sensor network deployment, MATLAB software (version R2016B) is used to analyze the three-dimensional relationship between expressway traffic density, intelligent vehicle permeability, and roadside RSU deployment spacing as well as to determine the optimal roadside RSU deployment spacing with the income model. Finally, the model reliability is validated by the Warshell algorithm and the Kmeans clustering algorithm.

Routing Protocols for Vehicle-to-Vehicle (V2V) Networks

VANET, short for Vehicle Ad-hoc Network, is an emerging technology with unique characteristics that differentiate it from previous ad-hoc networks. Designing an effective routing protocol for V2V (vehicle-to-vehicle) communication and V2I (vehicle-to-roadside infrastructure) communication is particularly challenging due to the dynamic nature of topology and frequent disconnections. VANET plays a crucial role in the development of Intelligent Transportation Systems (ITS) aimed at enhancing traffic flow and safety, primarily due to the high occurrence of traffic accidents. However, existing VANET routing protocols face limitations in effectively handling diverse traffic scenarios. To ensure future communication between vehicles for road safety, it is essential to develop appropriate routing protocols. This paper focuses on analyzing the advantages and disadvantages of routing protocols that can contribute to the development of new or improved routing protocols in the near future.